Apart from a global increase in mGluR5 binding (DVnorm: 1.51 ± 0.02 vs. 1.56 ± 0.02, p<0.004), which was more pronounced in individuals with lower baseline mGluR5 availability than in individuals with higher mGluR5 availability (Figure 2—figure supplement 1), prolonged wakefulness also had behavioral consequences. Although subjects were alerted over the intercom when polysomnographic signs of sleep occurred during PET scanning, unintended sleep could not be completely prevented. Stage 1 NREM sleep was more than three times more prevalent after sleep deprivation than after normal sleep (14.0 ± 3.1 vs. 4.0 ± 1.8%; t25 = 4.50, p<0.0001). When comparing the increase in stage one sleep episodes with the increase in global mGluR5 availability, a significant positive association emerged (Figure 2A).

rP = Pearson Product Moment Correlation coefficient; rS = Spearman Rank Correlation coefficient. Those brain regions that showed a significant (pcorr <0.00278) correlation between mGluR5 availability and EEG <1 Hz activity on left and right hemisphere in both baseline and sleep deprivation conditions are highlighted by a star (*).

Consistent with the concept of sleep homeostasis (Achermann and Borbély, 2011), sleep deprivation strongly increased delta (47.9 ± 7.2%) and <1 Hz activity (38.5 ± 6.7%; pall < 0.0001) compared to baseline. Intriguingly, subdividing the study participants by median split into groups with low and high change in global mGluR5 availability after sleep deprivation revealed that the group with a minor change in mGluR5 availability also showed a reduced increase in delta and <1 Hz activity when compared to the group with a more pronounced increase in mGluR5 (Figure 2—figure supplement 3). When comparing the waking-induced increase in EEG power between 0.25 and 20 Hz and the increase in global mGluR5 availability, significant correlations within the delta band were found. Mean power in the 2–4 Hz (rS = 0.44, p<0.04) (Figure 2B) and <1 Hz ranges (rS = 0.39, p<0.07) were specifically associated with increased mGluR5 availability (Figure 2—figure supplement 2). These results provide the first puzzle piece of evidence for the hypothesis that functional mGluR5 availability not only correlates with absolute low-frequency EEG power, but represents a molecular marker of elevated sleep need in response to sleep loss in humans.

To examine whether the strong association between mGluR5 availability and behavioral and neurophysiological markers of sleep need may be linked to endogenous changes in brain chemistry, 17 study participants underwent 1H-MRS imaging immediately following PET scanning. A previously established 1H-MRS protocol of a single voxel within the pregenual anterior cingulate gyrus of the medial prefrontal cortex was employed (Hulka et al., 2016). Changes in metabolite concentrations were considered relevant if simultaneously fulfilling the following criteria: (1) significant alteration by sleep deprivation and (2) significant correlation with global mGluR5 availability in both sleep conditions, or with the sleep deprivation-induced increase in mGluR5 availability. These stringent criteria eliminated all but two detected metabolites: myo-inositol and glycine. While the level of myo-inositol was reduced by sleep deprivation (Figure 4A), the values in both experimental conditions correlated positively with mGluR5 availability (Figure 4B and C). These results reflect a similar intra-subject variation in myo-inositol levels and mGluR5 binding in baseline and after sleep deprivation. In addition, the glycine concentration was enhanced after sleep loss (Figure 4D) and the increase was associated with the increased mGluR5 availability (Figure 4E). Collectively, these findings may suggest that mGluR5 contribute to sleep regulation by affecting downstream mechanisms of mGluR5-mediated protein phosphorylation and enhanced N-methyl-D-aspartate (NMDA) receptor-mediated signaling.

Lack of mGluR5 in mice interferes with sleep rebound after sleep deprivation

Given the striking association between mGluR5 availability and markers of sleep homeostasis in humans, sleep-wake regulation was studied in Grm5 gene (encoding mGluR5) knock-out (KO) mice and heterozygous (HT) and wild-type (WT) littermates. Behavioral states and EEG were quantified over 72 hr under regular light-dark cycles (12:12 hr). A 48 hr baseline period was followed by 6 hr sleep deprivation, starting at light onset (08:00 hr), and 18 hr of recovery.

In baseline, similar sleep-wake distributions were observed in all Grm5 genotypes (Figure 6—figure supplement 1). In the light phase, the major sleep phase in mice (Franken et al., 2001), however, KO animals spent slightly more time in NREM sleep and slightly less time in REM sleep and wakefulness than HT and WT littermates (F2,21 > 7.65, pall < 0.004, factor ‘genotype’ of two-way ANOVA with factors ‘genotype’ and ‘hour’) (Figure 6—figure supplement 2). No significant differences among the strains were present in the dark phase (F2,21 < 1.2, pall > 0.32).

A clear sleep-wake phenotype in Grm5 KO mice became apparent when sleep homeostasis was challenged with sleep deprivation. In the first 6 hr of the recovery period, all genotypes showed an immediate rebound of NREM and REM sleep at the cost of wakefulness (Figure 6 and Figure 6—figure supplement 1). Nevertheless, when the animals entered the dark phase (i.e. 6 to 18 hr after the end of sleep deprivation; ZT 12 to 24), sleep in KO mice was suppressed and wakefulness enhanced despite the preceding sleep deprivation (Figure 6—figure supplement 1). After 2 hr into the dark phase, NREM sleep started to decrease, whereas REM sleep remained virtually constant (Figure 6A and B). Averaged over the entire recovery period, KO animals lost 29.7 ± 16.7 min of sleep relative to baseline, mainly caused by a reduction in NREM sleep (Figure 6G). The KO mice also lacked a REM sleep rebound in the recovery dark phase (Figure 6B). In pronounced contrast to the animals without functional mGluR5, HT and WT mice continued to regain NREM and REM sleep in the dark phase (Figure 6A and B). By the end of the 18 hr recovery period, these animals had gained 83.5 ± 10.3 min and 82.4 ± 10.5 min of total sleep compared to baseline (F2,21 > 20.68, pall <0.0001, 1-way ANOVA with factor ‘genotype’ at ZT 24). In other words, the mean difference between sleep lost in KO animals and sleep gained in WT animals in the recovery period equaled almost two hours (113.2 min).

Important role for mGluR5 in homeostatic response to sleep deprivation

Next, we asked whether the different sleep-wake distributions across the 72 hr study protocol were accompanied by divergent dynamics of EEG delta (0.75–4.0 Hz) power in NREM sleep (see Materials and methods for details).

In the light phases during baseline, all genotypes showed a similar decline of delta-power in NREM sleep, reflecting the dissipation of homeostatic sleep need (Figure 7A). In the dark phases, however, the build-up was greatly attenuated in the KO animals. This attenuation was restricted to the first half of the dark phases (percentiles 1–3), during which the time spent in NREM sleep did not differ among the genotypes (Figure 6—figure supplement 1). Thus, the altered dynamics in EEG delta power cannot be explained by differences in time spent in NREM sleep, suggesting a deficient build-up of homeostatic sleep need in wakefulness. This notion was corroborated by substantially reduced delta power in the first two percentiles of the recovery period in KO and HT (to a lesser extent) animals (‘genotype’: F2,18 = 5.723, p<0.02) (Figure 7B). No genotype-dependent differences were observed in the recovery dark phase (Figure 7A), despite the different distributions of vigilance states (Figure 6—figure supplement 1). Although KO mice spent most of the time awake during the initial recovery dark phase (ZT 12 to 16), they lacked a rebound in delta power, demonstrating a severely disturbed sleep homeostatic response to prolonged wakefulness.

To assess the possible behavioral consequences of the sleep phenotype in Grm5 KO mice, we investigated spatial working memory and exploratory activity in a spontaneous alternation behavior paradigm (for details, see Materials and methods). Different groups of mice were tested in three runs, either after control sleep or after sleep deprivation. Run one was conducted in the active phase at the end of the analyzed recovery period when the differences in accumulated sleep rebound among the genotypes were greatest (ZT 22.5 to 24). Long-lasting consequences of sleep deprivation were assessed at the same circadian time, 1 (run 2) and 6 (run 3) days after run 1.

Working memory, expressed as the percentage of successful entry series (i.e. all three arms of the Y-maze were visited within three consecutive entries), was compromised in KO animals when compared to WT and HT littermates (‘genotype’: F2,26 > 4.2, p<0.03) (Figure 8A–C). Furthermore, alternation scores were higher than 50% in WT and HT mice (tall >4.6, pall <0.001; one sample t-tests), whereas KO mice performed at chance level (t = 0.95, p=0.18). The scores were similar in control and sleep deprivation conditions (‘condition’: F1,26 = 0.321, p=0.58 and ‘genotype’ x ‘condition’: F2,26 < 1.8, p>0.18).

Habituation to the novel environment was assessed by the total number of arm entries (see Materials and methods). Irrespective of experimental condition, KO mice showed reduced exploration at the first encounter with the maze when compared to WT animals (p<0.004) (Figure 8D). Arm entries were, thus, normalized to the first exposure, to examine the repercussions of sleep deprivation and genotype on habituation to the maze. In the control condition, all animals markedly reduced exploratory activity from the first to the second and third test runs. This reduction was attenuated when mice were sleep deprived before first maze exposure, especially in KO animals (‘condition’: run Δ2: F1,40 > 5.0, p < 0.04; run Δ3: F1,40 > 9.5, p < 0.004; ‘condition’ x ‘run’ interaction: F2,52 > 6.5, p < 0.003; ‘genotype’: F2, 40 > 4.0, pall < 0.03; ‘genotype’ x ‘run’ interaction: F4,52 > 3.0, p < 0.03) (Figure 8E–G). After prior challenge with prolonged wakefulness, these animals showed stable exploratory behavior across all test runs, suggesting that Grm5 KO mice are more impaired by sleep deprivation than WT and HT littermates and that the impairment persists for up to 1 week (Figure 8—figure supplement 1).

Discussion

This study provides compelling novel evidence for the notion that mGluR5 and the mGluR5 signaling cascade are an important part of the molecular mechanisms underlying the regulation of sleep need in humans and mice. In humans, increased mGluR5 availability after sleep deprivation was tightly associated with increased propensity to fall asleep during brain imaging, as well as delta and low frequency (<1 Hz) EEG activity in NREM sleep, reliable neurophysiological hallmarks of sleep homeostasis. Magnetic resonance spectroscopy further identified associated changes in brain myo-inositol and glycine levels, providing evidence for sleep loss-induced modifications in the downstream signaling cascade of mGluR5. Finally, the build-up of delta power during wakefulness in baseline and recovery periods in mice devoid of functional mGluR5 was severely disturbed, particularly during the dark phases of baseline and recovery periods. Together, the findings highlight in convergent translational fashion that mGluR5 likely contribute to functional aspects of sleep. For example, enhanced <1 Hz EEG activity in early NREM sleep improves memory functions in humans (Marshall et al., 2006), whereas in mice the mGluR5 signaling complex is important for the consolidation of contextual memory in vitro and in vivo (Huber et al., 2001; Lu et al., 1997).

The slow (<1 Hz) rhythm in the human EEG and its cellular counterpart, the slow oscillation, are characterized by brief sequences of membrane depolarization and intense neuronal firing (up-states), followed by membrane hyperpolarization and neuronal silence (down-states) (Achermann and Borbély, 1997; Steriade et al., 1993; Steriade, 1997; Vyazovskiy et al., 2009). In vitro and in vivo data suggest that mGluR1 and mGluR5 contribute to the regulation of the slow oscillation and the associated fluctuations in the membrane potential between up-states and down-states (Hays et al., 2011; Hughes et al., 2002). While other mechanisms regulating cortico-thalamic interactions and excitability of cortical neurons are likely also involved, these observations are consistent with the striking positive association found in this study between global mGluR5 availability and <1 Hz EEG activity, suggesting that mGluR5 may be important for maintaining or generating this slow rhythm. The slow oscillation and the corresponding down-state may provide a period of reduced synaptic inputs and be important for neuronal maintenance and rest (Vyazovskiy and Harris, 2013). Our data indicate that these rest periods are regulated or gated by mGluR5. Intriguingly, the slow <1 Hz rhythm has been associated with increased blood flow in precuneus, posterior cingulate, as well as medial frontal, parietal, and central gyri (Dang-Vu et al., 2008). These brain regions are almost identical with those where we observed the strongest associations between mGluR5 availability and EEG <1 Hz activity. In fact, mGluR5 availability in parietal cortex and precuneus explained as much as 64% and 70% of the intra-individual variance in <1 Hz EEG activity in baseline and recovery sleep.

Slow-wave or delta activity in NREM sleep is the best established marker of homeostatic sleep need (Achermann and Borbély, 2011). Here, we found a close association between global mGluR5 availability and initial EEG delta activity in NREM sleep in baseline and after sleep deprivation. Furthermore, the sleep deprivation-induced increase in global mGluR5 availability was positively correlated with increased subjective sleepiness (Hefti et al., 2013), reduced capacity to stay awake during brain imaging, as well as the rebound in delta activity in NREM sleep after prolonged wakefulness. These findings strongly indicate that mGluR5 activation constitutes a molecular mechanism keeping track of sleep-wake history. This notion is further supported by the altered sleep-wake distribution and dysfunctional dynamics of EEG delta power in NREM sleep in Grm5 KO mice.

Homer1a, the best established molecular substrate of homeostatic sleep-wake regulation today (Mackiewicz et al., 2008; Maret et al., 2007), selectively uncouples mGluR5 from the intracellular effector mechanisms such as the IP3 pathway (Diering et al., 2017; Ménard and Quirion, 2012; Tu et al., 1998). In this way, Homer1a buffers in activity-dependent manner the mGluR5-dependent release of calcium from intracellular stores (Bottai et al., 2002). The two metabolites found to be associated with mGluR5 availability and sleep deprivation, myo-inositol and glycine, are both linked to the mGluR5-Homer1a-IP3 signaling cascade (Berridge, 1984; Moghaddam and Javitt, 2012). Myo-inositol is the most abundant inositol derivative in the brain and a structural precursor of IP3 (Croze and Soulage, 2013). The positive association with mGluR5 availability suggests that the two molecules are tightly linked in the human brain. Moreover, myo-inositol was previously shown to be inversely coupled to neuronal activity (Xu et al., 2005). A plausible interpretation of the present data could be that the sleep deprivation-induced reduction in myo-inositol is caused by increased activation of mGluR5 after sleep loss, which triggers the formation of IP3 at the cost of myo-inositol. Future studies are warranted to corroborate this possible underlying mechanism.

Glycine is an important allosteric modulator of glutamatergic NMDA receptors (Johnson and Ascher, 1987) and activation of mGluR5 triggers enhanced activity of these receptors (Awad et al., 2000; Conn and Pin, 1997). The concomitant sleep loss-induced rise in glycine and the availability of mGluR5 suggest that NMDA receptor activation is enhanced after sleep deprivation. Indeed, in vitro electrophysiological analyses and immunoblotting of purified synaptosomes demonstrated that insufficient sleep alters NMDA receptor subunit composition and functions, whereas recovery sleep reverses these sleep deprivation-induced changes (Kopp et al., 2006). Furthermore, studies in rats revealed that glycine in low doses promotes wakefulness and reduces sleepiness (Bannai et al., 2012), whereas high doses of glycine rather promote NREM and REM sleep in a NMDA receptor-dependent manner (Kawai et al., 2015). In view of the diverse effects of glycine on wakefulness and sleep, the elusive mechanisms underlying its rise by sleep deprivation, as well as glycine’s varying functions in different neuronal circuits (Giber et al., 2015; Zeilhofer, 2005) the present data need to be interpreted with caution. Nevertheless, the spectroscopic findings may indicate that the mGluR5-associated Homer1a-IP3 and glycine-NMDA receptor pathways importantly contribute to the molecular machinery that regulates sleep-wake homeostasis in humans and open up promising new avenues for future research.

It may be noteworthy that the endogenous ligand of mGluR5, glutamate, and the related metabolites, glutamine and γ-amino-butyric acid (GABA), were not altered by sleep deprivation nor associated with the availability of mGluR5 (t15 < 0.67, pall > 0.5; data not shown). This observation suggests that the association between functional mGluR5 availability and sleep deprivation likely does not reflect upstream changes in glutamatergic signaling. Nevertheless, because PET and 1H-MRS imaging were slightly displaced and the spectroscopic data were acquired from a single voxel, future studies are needed to establish the generalizability of this notion.

The baseline sleep-wake pattern in mice without functional mGluR5 was similar as in their littermates, although a slight variation in the distribution of NREM sleep, REM sleep and wakefulness during the light phase was present. When sleep homeostasis was challenged by sleep deprivation, however, a clear dysregulation of wakefulness and sleep became apparent. The KO animals not only lacked the normal rebound in NREM and REM sleep in the recovery dark phase, they even lost roughly half an hour of NREM sleep when compared to baseline. This is in striking contrast to more than 1 hr of sleep gained in HT and WT mice by the end of the recovery period. To our knowledge, no comparable sleep-wake phenotype has ever been described in the literature in an animal model before. The data corroborate that functional mGluR5 are necessary for maintaining the normal physiologic homeostatic sleep response to sleep deprivation.

Already in baseline, the KO mice showed a markedly attenuated build-up in delta power during spontaneous wakefulness, although the distribution of vigilance states was comparable to the other genotypes. When challenged with sleep deprivation, both KO and HT animals showed an reduced build-up of delta power, resulting in lower values at the beginning of the recovery light phase when compared to WT littermates. In accordance with our findings, a diminished rebound in NREM sleep EEG delta power after prolonged wakefulness in Grm5 KO animals was reported previously (Ahnaou et al., 2015b). Nevertheless, the profound changes in sleep amounts and the dynamics of EEG delta power observed here were not found in the previous study. The discrepancy between the studies may be due to several methodological differences, in particular the use of an automated sleep deprivation technique by the other authors which induces forced locomotion. Forced locomotion causes stereotypic behavior unequal to normal wakefulness and the dissipation of sleep pressure (Fisher et al., 2016). Additionally, unlike the previous report, we also studied a HT group and found an intermediate phenotype between the WT and KO mice. This gene dose-effect relationship further highlights the importance of mGluR5 for the sleep-wake-dependent regulation of delta activity in NREM sleep.

Consistent with previously reported deficits in various learning and memory paradigms (Jia et al., 1998; Manahan-Vaughan and Braunewell, 2005), Grm5 KO mice performed worse than WT and HT littermates in a Y-maze working-memory task. In addition, KO animals initially explored less and were compromised in adapting exploratory behavior to the novel environment when sleep deprived. In agreement with the literature (Hagewoud et al., 2010; Niijima-Yaoita et al., 2016), the sleep deprivation challenge had no consistent effect on initial arm entries. One and 6 days after initial testing, however, the sleep deprived animals reduced exploratory activity to a lesser extent than the non-sleep deprived controls. Thus, acute sleep deprivation appears to induce a long-lasting impairment of the normal habituation to a novel environment (Bolivar, 2009). Mice lacking functional mGluR5 were particularly sensitive to this impairment, suggesting that mGluR5 contribute to the beneficial role of sleep to habituate to stressful conditions. Whether this phenotype is a consequence of compromised mGluR-dependent LTD (Lüscher and Huber, 2010; Manahan-Vaughan and Braunewell, 1999) remains to be determined.

In conclusion, our study provides converging translational evidence that increased mGluR5 availability following sleep loss in humans is associated with objective markers of sleep need and that lack of functional mGluR5 in mice severely affects sleep homeostasis. The question remains whether increased mGluR5 availability is a compensatory mechanism to promote wakefulness in the sleep-deprived state, signals the necessity to sleep, or both. Recent studies investigating the effects of positive and negative allosteric modulators of mGluR5 in rats may help to tackle this question (Ahnaou et al., 2015a). It was found that the positive allosteric modulator, ADX47273, promoted wakefulness and reduced NREM sleep and total sleep time. On the contrary, the mGluR5 negative allosteric modulator, MPEP (2-methyl-6-(2-phenylethynyl)pyridine;hydrochloride), increased total sleep time and sleep efficiency. Thus, sleep-wake-dependent changes in mGluR5 signaling may aid or facilitate sustained wakefulness and the proper homeostatic build-up of sleep propensity during wakefulness as reflected in EEG delta power in NREM sleep. At the same time, mGluR5-dependent mechanisms may promote and maintain deep sleep rich of slow waves. As such, mGluR5 could provide a promising new target for sleep-wake enhancing compounds, which may be beneficial in treating sleep-wake disorders such as hypersomnia or insomnia. Furthermore, behavioral studies will have to determine whether interventions targeting mGluR5 may promote sleep-associated brain functions such a memory consolidation and stress resilience.

Materials and methods

All experimental procedures were conducted in accordance with the declaration of Helsinki (1964) and approved by the cantonal (ethics committee for research on human subjects of the canon of Zurich [Reference Nr. EK-Nr. 786] and ethics committee of the State of Vaud Veterinary Office [No. 2699.0]) and Swiss federal authorities for research on human (Swiss Federal Institute of Public Health, Reference Nr. 464-0002-6/08.005701) and animal subjects.

Studies in humans

Study participants and pre-experimental procedure

A total of 26 healthy young men completed a 2-week study after providing written informed consent, and consent to publish. All study participants fulfilled strict inclusion criteria with respect to sleep quality and psychological wellbeing, and abstained from medication and drug use (Hefti et al., 2013). Three days before each experimental block, participants consumed neither caffeine nor alcohol and stringently adhered to an 8 hr sleep/16 hr wake schedule, verified by measuring caffeine in saliva, breath-alcohol levels, wrist-actigraphy, and sleep logs.

Sleep deprivation and imaging protocol

The experimental protocol was previously explained in detail elsewhere (Hefti et al., 2013). In short, all subjects completed in randomized, cross-over fashion two experimental blocks consisting of baseline and sleep deprivation conditions. To ensure sustained wakefulness, subjects were constantly supervised throughout the protocol. Eight-hour sleep episodes in baseline and recovery nights (23:00 – 07:00 [n = 9] or 00:00 – 08:00 [n = 17]) before and after prolonged wakefulness were recorded with polysmonography.

Because the two 1H-MRS scans were always performed consecutively, the effect of scan-order and sleep deprivation (condition) was assessed using two-way repeated measure ANOVAs. Applying internal water as a reference, total creatine was observed to be significantly altered by scan-order (F1,15 = 4.91, p<0.05). Because of this instability in creatine, metabolite levels were all referenced to internal water. The water referenced values were corrected for segmentation-based volume tissue composition and relaxation (Gasparovic et al., 2006). Individual T2 relaxation correction were performed in ProFit 2.0, with the resulting metabolite concentrations reported in arbitrary units (Fuchs et al., 2014; Hulka et al., 2016).

To evaluate the quality of the ProFit 2.0 fit, the Cramér-Rao lower bounds (CRLBs) was used as an internal control for each metabolite peak. Metabolite estimates with CRLBs > 20% were excluded. Because of movement artifacts or CRLBs > 20%, one spectrum had to be excluded in the sleep deprived condition. The quality criterion based on the CRLBs resulted in exclusion of a few data points. This applied especially to the analysis of the smaller GABA peak, in which n = 13 (control condition) and n = 10 (sleep deprivation condition) could be included. For the analyses of glutamate, glutamine, myo-inositol and glycine, n = 17 (control condition) and n = 14 (sleep deprivation condition) were included.

EEG and polysomnographic recordings

Continuous polygraphic recordings were conducted during PET scans and in all experimental nights. The EEG, electrooculogram (EOG), submental electromyogram (EMG), and electrocardiogram (ECG) were recorded with Rembrandt Datalab, Version 8 (Embla Systems, Planegg, Germany) using an Artisan polygraphic amplifier (Micromed, Mogliano Veneto, Italy). During PET image acquisition, subjects were instructed not to fall asleep and in case of sleep-like EEG activity, they were alerted via intercom. The EEG recordings were started shortly before initiation of PET imaging. The recording durations were virtually identical in the two conditions: baseline: 67.3 ± 1.0 min; sleep deprivation: 67.3 ± 0.7 min (p > 0.95). The amounts of intermittent N1 sleep expressed as a percentage of measurement time were analyzed.

The analog EEG signals were sampled at 256 Hz and conditioned by high-pass (−3 dB at 0.15–0.16 Hz) and low-pass filtering (−3 dB at 67.2 Hz). The EEG was recorded from one referential (C3M2) and eight bipolar derivations; the data of the C3M2 derivation are reported here. Sleep stages were visually scored in 20 s epochs according to standard criteria (Iber et al., 2007). Four-second EEG spectra (fast Fourier transform [FFT] routine, Hanning window, 0.25 Hz resolution; 0–20 Hz) were calculated with MATLAB (MathWorks Inc., Natick, MA), averaged over five consecutive epochs, and matched with scored sleep stages. Arousal- and movement-related artifacts were visually identified and eliminated. Mean slow-wave activity (SWA; EEG power within 0.5–4.5 Hz) and power in the frequency range of the sleep slow oscillation (power within 0.25–1.0 Hz) in the first NREM sleep (stages N1–N) episodes (Feinberg and Floyd, 1979) were calculated in each participant in the baseline night of the first study block and in the recovery night after sleep deprivation. Unless otherwise specified, statistics from EEG data are performed on logarithmic base 10 transformed values.

Studies in mice

The Grm5‐/‐(KO) mice were generated as previously described (Jia et al., 1998) and backcrossed to C57BL/6J mice. Experiments were carried out with adult (10–12 weeks) male Grm5+/+ (WT), Grm5+/‐ (HT) and KO littermates obtained by heterozygous breeding. Mice were group‐ or single housed according to the experimental procedure in polycarbonate cages (31 × 18 × 18 cm) in a temperature‐ (25°C) and humidity‐controlled (50‐60%) environment under a 12:12 hr light dark cycle (lights on 8:00, 70‐90 lux). Food and water were available ad libitum.

Sleep deprivation procedure

The EEG/EMG signals were recorded for 48 hr of undisturbed baseline (i.e. baseline 1 and baseline 2, 24 hr each). A 6 hr sleep deprivation started at light onset (ZT [zeitgeber] 0 to 6) of the third day and was achieved by so called ‘gentle handling’. During this procedure, animals are not handled but the bedding material is gently moved, the cage tapped, or novel objects introduced as soon as behavioral signs of sleep appeared. In addition, mice are provided with a clean cage halfway during the sleep deprivation (ZT 3), which provides additional stimulation. The sleep deprivation was followed by a 18 hr recording period during which recovery from sleep deprivation was quantified (n = 8 mice/genotype).

EEG/EMG implantation and recordings

Surgery for fronto‐parietal EEG recordings was performed under deep ketamine/xylazine anesthesia (intraperitoneal injection, 75 and 10 mg/kg at a volume of 10 ml/kg) at 10‐12 weeks of age. EEG/EMG signals were obtained as previously described (Mang and Franken, 2012). Briefly, six gold-plated mini‐screws (1.1 mm diameter) were implanted over frontal and parietal cortices. Over the right hemisphere, two screws soldered to the recording leads prior to implantation, served as frontal (1.5 mm anterior to bregma, 1.7 mm lateral to midline) and parietal (1.0 mm anterior to lambda, 1.7 mm lateral to midline) electrodes. The remaining four screws served as anchors. For EMG recordings two gold wires were inserted into the neck muscle. Anchor screws, EEG and EMG electrodes were fixed to the skull by using dental cement. The incision was closed by stitching and animals received a 10 mg/kg dose of analgesic (Flunixine). Animals were allowed 5 days to recover, followed by a 6‐day habituation period, to adapt to the connecting cable. EEG/EMG signals were recorded with Somnologica (Somnologica Science 3.3.1.1529, Medcare), amplified, analog‐to‐digital converted (2 kHz) and down-sampled to 200 Hz. Power spectral analysis between 0–100 Hz was performed with FFT of 4 s epochs of frontal‐parietal differential EEG recordings, yielding a frequency resolution of 0.25 Hz. The vigilance states wakefulness (W), REM sleep and NREM sleep were determined for consecutive 4 s epochs using standard criteria (Mang and Franken, 2012). Epochs containing EEG artifacts were marked and excluded from spectral analysis.

Analyses of vigilance states

The minutes spent in each vigilance stage per hour across the 72 hr experiment were analyzed with two-way repeated measures ANOVAs (n = 8 mice/genotype; factors ‘genotype’ and ‘hour’), to detect genotype-dependent differences in the light and dark phases in baseline (12 hr each) and recovery (6 hr light and 12 hr dark). One-way ANOVAs with the factor ‘genotype’ were carried out on hourly values as post-hoc analysis after the overall ANOVA reached significance (p<0.05 for the factor ‘genotype’ or interaction ‘genotype’ x ‘hour’).

To investigate differences among genotypes in their response to sleep deprivation, statistical analyses in light and dark periods of recovery were performed on the mean baseline relative values, calculated in each individual mouse with two-way repeated measure ANOVA (factors ‘hour’ and ‘genotype’).

To describe the progression of rebound behavior after sleep deprivation, accumulated change in vigilance states (recovery – baseline) was calculated and depicted. One-way ANOVAs with the factor ‘genotype’ were performed on hourly values. All significant genotype effects were further decomposed by performing t-tests, Holm-corrected for multiple testing, as post-hoc analysis.

Spectral analysis

For spectral analysis, artifacts and state transition epochs were excluded, that is, only epochs preceded and followed by epochs of the identical vigilance state were considered. Data were normalized and weighted according to contributing vigilance states as previously described (Franken et al., 1999; Mang et al., 2016). In brief, absolute spectral power density (PSD) between 0.75 and 49 Hz in 0.25 Hz bins was measured for each vigilance state and condition. To account for inter-individual differences in total EEG power, overall reference values were calculated (Franken et al., 1999; Mang et al., 2016). This was done by calculating the area under the PSD curve per vigilance state for baseline and multiplying it by the percentage of contributing epochs per 24 hr. The sum of weighted values corresponds to the total power reference values used for further analysis. Absolute state specific power (i.e. Wake, NREM, REM separately), was divided by the total power reference value of the respective mouse, to obtain the relative power data used for statistical analysis.

Time course analysis of EEG delta power

The time course of delta power (0.75–4 Hz) in NREM sleep was computed as previously described (Franken et al., 1999; Maret et al., 2007). To investigate the temporal progression of delta power, values were computed relative to the last 4 hr of light phase (i.e. ZT 8 to 12 defined as 100%) in baseline (mean of baseline days 1 and 2 per mouse). This interval corresponds to the period with lowest delta power (Franken et al., 2001; Mang and Franken, 2012).

The number of NREM sleep epochs in the light and dark phases were quantified for all recording days. To adjust for the different occurrence of NREM sleep across the light and dark cycle, recording segments were subdivided into intervals (12 intervals for 12 hr light phases, 6 intervals for 12 hr dark phases, and eight intervals for the 6 hr recovery light phase subsequent to sleep deprivation; see also Figure 7) with an equal number of contributing NREM epochs for all intervals of the respective segment. Normalized mean delta power was calculated by dividing mean absolute delta power with the respective reference value for each interval and mouse. The ZT-time of each interval was calculated as the mean ZT-time of the contributing NREM epochs. Statistical analysis was done by 1-way ANOVAs, with factor ‘genotype’ for each time point (i.e. interval), followed Holm-corrected t-test (n = 7–8 mice/genotype; due to aberrant spectral power in one mouse in the recovery period [>2 standard deviations from the mean], this animal was excluded from the quantitative EEG analyses).

Quantitative real-time PCR

Whole brain total RNA of undisturbed WT, HT and KO mice (n = 4) and total RNA of cortex, striatum and hippocampus of sleep deprived mice and their littermate controls (n = 4) was extracted using RNase Lipid Tissue Midi Kit (Qiagen) and treated with RNase-free DNase (Qiagen, Hilden, Germany). RNA content and quality were controlled by NanoDrop for quality control ration 260/280 ~2. Because some extracts from dissected brain areas did not fulfill these criteria they had to be excluded. n = 3 per group could be used for analyses.

From 1 µg of sample RNA and controls (no-template control and no-enzyme control) cDNA was produced by reverse transcription PCR using random primers. cDNA was subsequently used for quantitative PCR (TaqMan) probing for Grm5 (probe/primer assay Applied Biosystems) and the reference genes EEF1a1 (eucaryotic elongation factor 1a1), TBP (TATA-box-binding protein) and Rps9 (ribosomal protein S9). Probes and primers for reference genes were designed in-house and produced by Microsynth (Balgach, Switzerland). Quantification of the amplification was performed in triplicates, that is, technical replicates, in QuantStudio 6Flex Real-time PCR System (Thermo Fisher Scientific, Massachusetts). Fold-expression relative to the reference genes was analyzed by one-way ANOVA with factor ‘genotype’ for whole brain RNA which has been extracted from mice under undisturbed conditions, and by two-way ANOVA with factors ‘genotype’ x ‘condition’ for dissected brain areas in sleep deprived and sleep control mice.

Spontaneous alternation and exploratory behavior in the Y-maze

Mice were habituated by an adapted tunnel-handling protocol (Hurst and West, 2010), which minimizes the stress associated with picking up and moving mice. In brief, all animals were exposed to a Plexiglas tunnel for 3 days before the first experimental session. They were habituated by gently picking them up repeatedly for 30 s with the tunnel and allowing them to recover for 1 min in between. This protocol was repeated on 2 days, twice per day at random time points, during the week prior to the experiments.

To test working memory, continuous spontaneous alternation behavior (SAB) was assessed in the Y-Maze (three arms of equal size symmetrically placed at 120°; inner length: 36.5 cm, width: 6 cm, height: 15 cm, color: grey) described in detail elsewhere (Hughes, 2004; Ramanathan et al., 2010). Each mouse was picked up with the tunnel, released to the center of the maze and allowed to explore freely for 7 min, before being returned with the tunnel to the home cage. The maze was surrounded by black walls to avoid any extra-maze cues and cleaned with 1% acetic acid between mice. Testing was performed from ZT 22.5–24 in the dark phase, the main activity phase of mice, under indirect dim light (15 lux). Each animal was tested three times: run one at the end of the first recovery day after sleep deprivation (i.e. after 16.5 hr of recovery), run two followed 24 hr later, whereas run three was performed 1 week after sleep deprivation (6 days after run two). Number of control animals: 6 WT, 8 HT and 6 KO mice; numbers of sleep deprived animals: 7 WT, 9 HT and 10 KO mice. Mouse behavior in the maze was video-recorded from above and arm entry sequences assigned visually. Spontaneous alternations [%] were defined as successful entry sequences (i.e. all three arms visited in three consecutive entries) relative to possible successful entry sequences (i.e. total number of entries minus two). An arm was considered to be entered, when the mouse had placed all four paws within the walls of the arm (Hughes, 2004). Mice were tested in four batches. Two parameters were analyzed from the Y-maze data: ‘Alternation score’ was used as a measure of working memory, whereas the ‘number of total arm entries’ was used as a measure for exploratory behavior.

Statistical analyses

Statistical analyses were performed with SAS 9.1.3 software (SAS Institute, Cary, NC) for human data and in R-project 3.1.2 software (www.r-project.org) for mice analysis. To examine associations between mGluR5 availability and EEG markers of sleep need, Spearman rank correlations were calculated. Pearson’s product moment correlation analyses revealed very similar results, and are not reported here. To correct for scan order in the MR spectroscopy imaging, two-way repeated measure mixed model ANOVAs were performed with factors ‘scan-order’ (first vs second) and condition (sleep control vs sleep deprived). For remaining effects of sleep-deprivation, two tailed paired Student’s t-tests were performed. Before statistical testing, variables were tested for normality. If the distribution significantly differed from a Gaussian distribution, appropriate transformations were applied for statistical analyses.

The behavioral data in mice were statistically analyzed by ANOVA with factors ‘genotype’ x ‘condition’ x ‘run’ x ‘batch’, and followed by appropriate post-hoc t-tests if significance was found for the respective factors or interactions.

Decision letter

Louis J Ptáček

Reviewing Editor; University of California, San Francisco, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Cerebral mGluR5 Availability Contributes to Elevated Sleep Need and Behavioral Adjustment after Sleep Deprivation" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editor and the evaluation has been overseen by Eve Marder as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Michael Lazarus (Reviewer #2); Anita Lüthi (Reviewer #3).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

The present work is an interesting study combining human and mouse experimentation that lends further support to a molecular element in homeostatic sleep regulation: the metabotropic glutamate receptor 5 and associated signaling pathways. Direct measurements of mGluR5 availability and associated signaling in humans through PET and subsequent MRI are combined with spectral and behavioral assessments of sleep deprivation and early baseline sleep. Sleep-wake behavior in mGluR5-deficient mice unravels additional complex roles of this receptor in sleep-wake regulation and behavioral adaptation to sleep pressure.

Summary:

In this manuscript, the authors found that the availability of mGluR5 in human brain is positively correlated with EEG δ activity of recovery sleep. Consistent with the human results, they also found that mice without mGluR5 gene showed retarded increase of EEG δ power in the dark phase and after sleep deprivation. Consequently, the mGluR5 knockout mice showed greatly reduced rebound sleep after sleep deprivation. This study provides strong evidence for the function of mGluR5 in regulating mammalian sleep homeostasis, suggesting that mGluR5 is indispensable for the accumulation of sleep need after sustained wakefulness.

A sleep phenotype of mGluR5-/- mice has been reported by another group in 2015 (Ahnaou et al., 2015). The phenotype of mGluR5-/- mice described by Ahnaou et al. is somewhat different than reported here. In their study, the mGluR5-/- mice showed no increased wakefulness and no reduced NREM sleep after sleep deprivation. So the authors need to present and discuss these apparently contradictory results.

Essential revisions:

1) The author's statements in subsection “Sleep deprivation-induced changes in brain metabolites reflect downstream markers of mGluR5 activation “("Collectively, mGluR5 contribute to sleep regulation by affecting downstream mechanisms of mGluR5-mediated protein phosphorylation and enhanced N-methyl-D-aspartate (NMDA) receptor-mediated signaling") and subsection “mGluR5 availability predicts behavioral and EEG markers of sleep need in humans” ("[…]but represents a molecular marker of elevated sleep need in response to sleep loss in humans.") are overreaching based on evidence provided by the human studies. One important question that the authors should address is mGluR5 availability or expression levels in sleep-deprived mice (wild-type and KO mice) as compared to control mice. This may provide a link between human imaging studies and mouse behavior to support their hypothesis that mGluR5 is important for sleep need.

2) The authors state in subsection “mGluR5 availability predicts behavioral and EEG markers of sleep need in humans” that "A similar relationship was also observed for the entire 0.5-4.5 Hz band (yet not any other frequency band)[…] ". It would be informative if the data for the other frequencies are shown.

3) The authors argue that altered EEG δ power dynamics are suggesting "a deficient build-up of homeostatic sleep need in wakefulness". It is however possible that changes in δ power in the KO mice is independent from the homeostatic sleep need, because there are no differences in baseline sleep between the genotypes. Moreover, the statement "… demonstrating a severely disturbed sleep homeostatic response to prolonged wakefulness." (subsection “Important role for mGluR5 in homeostatic response to sleep deprivation”) is at odds with Figure 5—figure supplement 1, in which the KO mice actually show a higher NREM sleep amount after sleep deprivation in the remaining light phase, indicating a homeostatic sleep response exists which may just be stronger than in wild-type mice during this period (i.e. the dissipation of sleep need may be enhanced).

4) In the Discussion section, the authors suggest that sleep loss causes an increase in glycine levels. What could be the reason for this rise in glycine by sleep deprivation? Without a reasonable explanation, it is possible that this is unrelated to the mGluR5/NMDA signalling system.

5) The statement "Thus, increased mGluR5 signaling during prolonged periods of spontaneous or enforced wakefulness may aid or facilitate sustained wakefulness" (subsection “Lack of mGluR5 compromises adjustment to novel environment after sleep deprivation”) is inconsistent with the fact that mGluR5 KO mice have a lower amount of recovery sleep and SWA. The authors need to define more carefully the role of mGluR5 in sleep (or wake) regulation. What does a prolonged period of spontaneous wakefulness look like?

6) One asset of this work is the combination of studies on human and mouse. Could this be pushed even further? The human studies include recovery sleep after sleep deprivation. Were there interindividual variabilities in this recovery sleep that correlated with the availability of mGluR5? Can anything be said about the activity profile in the day after recovery sleep?

7) The interindividual variability in mGluR5 availability and its correlation with baseline sleep properties a remarkable observation that can help explain basic differences in sleep need. Have any assessments been done with the subjects in this direction, e.g. via questionnaires? Were there any differences in mood-related behaviors?

8) The interindividual variability should be better emphasized in the paper. For example, in Figure 2, it is not clear which points in Figure 2A relate to the points in Figure 2B. From Figure 2C, it can be guessed that there is a subgroup of subjects that show particularly large increases in mGluR5 availability. Were low mGluR5-expressers particularly vulnerable to SD in terms of mGluR5 changes?

9) Why were maximal availability increases after SD not higher than the maximal availability values in baseline?

10) The use of frequency bands in the power spectra of NREM sleep needs to be better described. In Figure 2, the full δ (0.5-4 Hz) and the high δ (2-4 Hz) bands are analyzed. In Figure 3, it is the slow wave band (< 1 Hz) that is emphasized. Although it is very interesting to note that in particular power in the high δ band correlates with mGluR5 increases, this needs to be clearly motivated. Same for Figure 3.

11) Homer 1a is also upregulated with SD, and Homer1a uncouples mGluR5 from its intracellular signaling partners. However, myo-inositol levels are decreased in a manner that correlate with decreases in mGluR5 levels. Are decreased myo-inositol levels also correlated with enhanced low-frequency power levels in the spectrogram? Is this to conclude that augmented mGluR5 activation overcomes Homer1a upregulation? The results seemed to be pointing to this somewhat paradoxical situation, and should be further discussed.

12) Three subjects were excluded from PET imaging, so why n =26 in subsection “mGluR5 availability predicts behavioral and EEG markers of sleep need in humans”?

13) In a supplementary figure, can example metabolites be presented to better illustrate the criteria for exclusion/inclusion as described in subsection “Sleep deprivation-induced changes in brain metabolites reflect downstream markers of mGluR5 activation”?

Author response

Summary:

In this manuscript, the authors found that the availability of mGluR5 in human brain is positively correlated with EEG δ activity of recovery sleep. Consistent with the human results, they also found that mice without mGluR5 gene showed retarded increase of EEG δ power in the dark phase and after sleep deprivation. Consequently, the mGluR5 knockout mice showed greatly reduced rebound sleep after sleep deprivation. This study provides strong evidence for the function of mGluR5 in regulating mammalian sleep homeostasis, suggesting that mGluR5 is indispensable for the accumulation of sleep need after sustained wakefulness.

A sleep phenotype of mGluR5-/- mice has been reported by another group in 2015 (Ahnaou et al., 2015). The phenotype of mGluR5-/- mice described by Ahnaou et al. is somewhat different than reported here. In their study, the mGluR5-/- mice showed no increased wakefulness and no reduced NREM sleep after sleep deprivation. So the authors need to present and discuss these apparently contradictory results.

Consistent with our results, Ahnaou et al. (2015) reported reduced EEG slow-wave activity (SWA) and sleep drive in mGluR5 knock-out (KO) mice after sleep deprivation. However, the profound changes in sleep amounts and EEG δ power in the dark period after sleep deprivation were not found by the previous authors. The discrepancy may be due to several methodological differences between the studies, including different durations and techniques of sleep deprivation (automated procedure vs. gentle handling), different EEG-EMG recording devices (implants vs. cables), etc. Most importantly, Ahnaou et al. employed an automated sleep deprivation technique with forced locomotion during 8 hours, while we have used the well-established gentle handling sleep deprivation for 6 hours. Prolonged forced locomotion induces a stereotypic behavior different from normal wakefulness, which decreases cortical activity and the rebound in subsequent sleep δ activity (Fisher et al., 2016; Franken et al., 2001). In the revised manuscript, we write (subsection “Genetic loss-of-function of mGluR5 in mice causes dysregulation of sleep-wake patterns and sleep EEG δ activity”): “In accordance with our findings, a diminished rebound in NREM sleep EEG δ power after prolonged wakefulness in mGluR5 KO animals was reported previously (Ahnaou et al., 2015). Nevertheless, the profound changes in sleep amounts and the dynamics of EEG δ power observed here were not found in the previous study. The discrepancy between the studies may be due to several methodological differences, in particular the use of an automated sleep deprivation technique by the other authors which induces forced locomotion. Forced locomotion causes stereotypic behavior unequal to normal wakefulness and the dissipation of sleep pressure (Fisher et al., 2016).”

We agree with the reviewers that the above statements are overreaching based on the data presented. However, we have been more careful in interpreting our results than implied above. In subsection “mGluR5 availability predicts behavioral and EEG markers of sleep need in humans“, we wrote in the original version of the manuscript: “These results provide the first puzzle piece of evidence for the hypothesis that functional mGluR5 availability not only correlates with absolute low-frequency EEG power, but represents a molecular marker of elevated sleep need in response to sleep loss in humans.” Furthermore, in subsection “Sleep deprivation-induced changes in brain metabolites may reflect downstream markers of mGluR5 activation”, we were careful in writing: “Collectively, these findings suggest that mGluR5 contribute to sleep regulation by affecting downstream mechanisms of mGluR5-mediated protein phosphorylation and enhanced N-methyl-D-aspartate (NMDA) receptor-mediated signaling.” To be even more careful, we added “may suggest” in the revised version of the manuscript.

One important question that the authors should address is mGluR5 availability or expression levels in sleep-deprived mice (wild-type and KO mice) as compared to control mice. This may provide a link between human imaging studies and mouse behavior to support their hypothesis that mGluR5 is important for sleep need.

We thank the reviewers’ for the suggestion. We performed qPCR analyses of mGluR5 mRNA expression extracted from cortex, hippocampus and striatum in mGluR5 wild type (WT), heterozygous (HT) and KO mice and found no consistent effect of sleep deprivation on mGluR5 mRNA expression. We report these experiments in new Figure 5.

Other authors recently also failed to find differences in mGluR5 protein expression between wakefulness and sleep (Diering et al., 2017, Figure S1). Therefore, the sleep deprivation-induced changes in mGluR5 availability found in our human experiment are likely to indicate a functional synaptic change due to receptor trafficking rather than a change in overall mRNA or protein levels. To replicate the human findings in mice, autoradiographic quantification could be performed in future studies. We write in the revised version of the manuscript (subsection “Lack of mGluR5 in mice interferes with sleep rebound after sleep deprivation “): “Furthermore, we performed qPCR analyses of mGluR5 mRNA expression extracted from cortex, hippocampus and striatum in mGluR5 WT, HT and KO mice after sleep control and sleep deprivation. While mGluR5 expression importantly varied according to allele ‘dose’, we did not observe a consistent effect of sleep loss on mGluR5 mRNA expression in WT and HT mice (Figure 5). Similarly, other authors recently failed to find differences in mGluR5 protein expression between wakefulness and sleep (Diering et al., 2017). Therefore, the sleep deprivation-induced changes in mGluR5 availability we found in our human experiment are likely representing a functional synaptic change due to receptor trafficking rather than changes in overall mRNA or protein levels.”

2) The authors state in subsection “mGluR5 availability predicts behavioral and EEG markers of sleep need in humans” that "A similar relationship was also observed for the entire 0.5-4.5 Hz band (yet not any other frequency band)…, ". It would be informative if the data for the other frequencies are shown.

We thank the reviewers for this suggestion. We agree that the higher frequency bands are interesting and potentially valuable to present. They were omitted in the original manuscript because the statistical analyses revealed no significant correlations with regional mGluR5 availability. We now present a supplement to Figure 3, including the correlation analyses for five predefined frequency ranges between 0.5 and 20 Hz. We emphasize in the revised manuscript that with the exception of the δ band, the statistical results do not withstand correction for multiple comparison.

3) The authors argue that altered EEG δ power dynamics are suggesting "a deficient build-up of homeostatic sleep need in wakefulness". It is however possible that changes in δ power in the KO mice is independent from the homeostatic sleep need, because there are no differences in baseline sleep between the genotypes. Moreover, the statement "… demonstrating a severely disturbed sleep homeostatic response to prolonged wakefulness." (subsection “Important role for mGluR5 in homeostatic response to sleep deprivation”) is at odds with Figure 5—figure supplement 1, in which the KO mice actually show a higher NREM sleep amount after sleep deprivation in the remaining light phase, indicating a homeostatic sleep response exists which may just be stronger than in wild-type mice during this period (i.e. the dissipation of sleep need may be enhanced).

Baseline differences in time-spent-asleep cannot be taken as evidence of a different homeostatic ‘need’. That would imply that all sleep expressed under undisturbed baseline conditions is homeostatically defended, which we know is not the case and we also know that factors other than homeostatic drive can influence undisturbed time-spent asleep. Moreover, would less sleep time signal a higher sleep pressure as a result of sleeping less or a reduced need for sleep?

The sleep-wake dependent changes in δ power are widely used as markers of momentary sleep ‘need’ because the longer one is awake, the higher δ power in subsequent NREM sleep will be. Thus, the lack of a genotype difference in the amount and distribution of sleep during baseline in the presence of a marked blunting of the build-up of δ power in the dark or active period shows that the quantitative relationship between time-spent-awake and δ power is severely altered in KO mice. If the reviewers can agree that δ power is a marker of a homeostatic process then one is allowed to interpret this finding to indicate that per unit time spent awake sleep need increases less when mGluR5 is missing. The observations made after an enforced period of wakefulness (i.e., sleep deprivation) confirms this interpretation. By contrast, δ power normally decays during sleep (i.e., during the light periods of baseline and recovery).

4) In the Discussion section, the authors suggest that sleep loss causes an increase in glycine levels. What could be the reason for this rise in glycine by sleep deprivation? Without a reasonable explanation, it is possible that this is unrelated to the mGluR5/NMDA signalling system.

We agree with the reviewers that the spectroscopic data of our study need to be interpreted with caution, yet they open up possibly promising new avenues for future research. We added a cautionary note in the Discussion section: “In view of the diverse effects of glycine on wakefulness and sleep, the elusive mechanisms underlying its rise by sleep deprivation, as well as glycine’s varying functions in different neuronal circuits (Giber et al., 2015; Zeilhofer, 2005), the present data need to be interpreted with caution. Nevertheless, the spectroscopic findings may indicate that the mGluR5-associated Homer1a-IP3 and glycine-NMDA receptor pathways importantly contribute to the molecular machinery that regulates sleep-wake homeostasis in humans and open up promising new avenues for future research.”

5) The statement "Thus, increased mGluR5 signaling during prolonged periods of spontaneous or enforced wakefulness may aid or facilitate sustained wakefulness" (subsection “Lack of mGluR5 compromises adjustment to novel environment after sleep deprivation”) is inconsistent with the fact that mGluR5 KO mice have a lower amount of recovery sleep and SWA. The authors need to define more carefully the role of mGluR5 in sleep (or wake) regulation. What does a prolonged period of spontaneous wakefulness look like?

We agree with the reviewers that this part of the Discussion section was partially unprecise. We revised the respective paragraph: “Thus, sleep-wake dependent changes in mGluR5 signaling may aid or facilitate sustained wakefulness and the proper homeostatic build-up of sleep propensity during wakefulness as reflected in EEG δ power in NREM sleep. At the same time, mGluR5-dependent mechanisms may promote and maintain deep sleep rich of slow waves.”

6) One asset of this work is the combination of studies on human and mouse. Could this be pushed even further? The human studies include recovery sleep after sleep deprivation. Were there interindividual variabilities in this recovery sleep that correlated with the availability of mGluR5? Can anything be said about the activity profile in the day after recovery sleep?

We thank the reviewers for their detailed interest in these results.

To further investigate the relationships between recovery sleep after sleep deprivation and inter-individual variation in mGluR5 availability, we created additional supplements to Figure 2. First, we subdivided the data by median split into study participants with low and high change in global mGluR5 availability after sleep deprivation, and compared the effect of prolonged wakefulness on EEG δ and slow-oscillation activity. These analyses revealed that the group with a minor change in mGluR5 availability also showed a significantly reduced increase in δ and < 1 Hz activity when compared to the group with a more pronounced increase in mGluR5. By contrast, neither theta, α, σ (p = 0.06) nor β activity were differently changed by sleep deprivation in the two groups.

7) The interindividual variability in mGluR5 availability and its correlation with baseline sleep properties a remarkable observation that can help explain basic differences in sleep need. Have any assessments been done with the subjects in this direction, e.g. via questionnaires? Were there any differences in mood-related behaviors?

We thank the reviewers for this interesting question.

The associations between sleep deprivation-induced changes in subjective measures and mGluR5 availability were the focus of our previous publication (Hefti et al. 2013). The current manuscript focuses on the associations between objective markers of elevated sleep need after prolonged wakefulness and mGluR5 availability.

Given the interesting nature of the above question, however, we performed additional analyses to shed some light on possible associations between mGluR5 availability in baseline and mood. Mood assessments with the 24-item Profile of Mood States (POMS) were performed at 3-hour intervals during extended waking and before each imaging session. Investigating POMS scores collected immediately before the baseline PET scans, and the average scores for the six assessments across the baseline day, did not reveal significant correlations with global mGluR5 availability in baseline (pall > 0.1). Variables tested included the total mood score and the subcategories depression, fatigue, vigor and irritability. Given the negative statistical tests results, these new analyses are not included in the manuscript. It needs to be kept in mind, however, that a homogenous group of carefully screened healthy volunteers was studied.

Whether mGluR5 availability in specific brain regions could be associated with mood measures (such as found in depressed patients; Deschwanden et al., 2011) or whether other mood questionnaires or test scores may be more sensitive than the POMS to capture interindividual variation in mGluR5 availability remain possible subjects for future research.

8) The interindividual variability should be better emphasized in the paper. For example, in Figure 2, it is not clear which points in Figure 2A relate to the points in Figure 2B. From Figure 2C, it can be guessed that there is a subgroup of subjects that show particularly large increases in mGluR5 availability. Were low mGluR5-expressers particularly vulnerable to SD in terms of mGluR5 changes?

We agree with the reviewers that the inter-subject variability in mGluR5 availability and individual responses to sleep deprivation can be better illustrated. To show each individual’s response, we created an additional supplement to Figure 2. In addition, we split the subjects based on either low (n = 11) or high (n = 12) global mGluR5 availability at baseline (median split) and found that those study participants with a lower baseline expression of mGluR5 exhibit a significantly larger increase by sleep loss than those participants with a higher baseline mGluR5 availability.

9) Why were maximal availability increases after SD not higher than the maximal availability values in baseline?

The global change in mGluR5 availability by sleep deprivation equals roughly 5% , which makes it difficult to capture with the naked eye. As illustrated above (reviewer item # 8), the participants with the highest mGluR5 availability in baseline typically showed a minor increase, or even a slight decrease, in mGluR5 availability after sleep deprivation. Interestingly, a similar bi-directional relationship was recently reported for the effects of sleep deprivation on markers of associative synaptic plasticity in the human cortex (Kuhn et al., 2016). These authors suggested that their findings could be explained by the Bienenstock-Cooper-Munro theory of bi-directional synaptic plasticity, stating that the threshold for LTP (long-term potentiation/LTD (long-term depression) induction is adjusted to the level of prior synaptic activity (Bienenstock et al., 1982). Our data may offer the fascinating possibility that sleep deprivation-induced changes in mGluR5 availability provide a molecular substrate contributing to the observed effects of sleep loss on LTP- and LTD-like plasticity. Alternatively, high baseline mGluR5 availability may be difficult to increase further by increased time awake and the data may reflect a ceiling effect. Further studies are necessary to investigate the different hypotheses.

10) The use of frequency bands in the power spectra of NREM sleep needs to be better described. In Figure 2, the full δ (0.5-4 Hz) and the high δ (2-4 Hz) bands are analyzed. In Figure 3, it is the slow wave band (< 1 Hz) that is emphasized. Although it is very interesting to note that in particular power in the high δ band correlates with mGluR5 increases, this needs to be clearly motivated. Same for Figure 3.

We agree with the reviewers that the presentation of the different frequency bands could have been better explained in the manuscript.

The study’s primary aim was to address the hypothesis that mGluR5-related mechanisms are associated with sleep homeostasis. For that reason, we focused on the EEG δ range below 5 Hz. In the initial analysis steps, we only considered frequency bands relevant if multiple adjacent frequency bins showed a significant (p < 0.05) correlation between mGluR5 availability and consecutive 0.25-Hz bins in the EEG δ range at baseline and sleep deprivation. This analysis revealed highly significant correlations in the bins < 1 Hz. Interestingly, the 0.5-4.5 Hz band remained significant yet generally with lower correlation coefficients than the < 1 Hz bins. When examining the relative correlations, i.e., how EEG activity and mGluR5 availability changed with sleep deprivation, the high δ range > 2 Hz was significant.

To further illustrate the frequency specificity of these associations, the figure below shows the Spearman rank correlation coefficients between global mGluR5 availability and EEG power between 0-20 Hz at baseline, sleep deprivation and the change caused by sleep loss. The green lines indicate r-values that are above (or below) the a = 0.05 significance threshold. The Figure 2—figure supplement 2 illustrates that the 2-4 Hz range is relevant for the change after sleep deprivation.

11) Homer 1a is also upregulated with SD, and Homer1a uncouples mGluR5 from its intracellular signaling partners. However, myo-inositol levels are decreased in a manner that correlate with decreases in mGluR5 levels. Are decreased myo-inositol levels also correlated with enhanced low-frequency power levels in the spectrogram? Is this to conclude that augmented mGluR5 activation overcomes Homer1a upregulation? The results seemed to be pointing to this somewhat paradoxical situation, and should be further discussed.

This is an interesting question, which is difficult to answer at present.

As discussed in the manuscript, our results are in line with published data in rats showing that myo-inositol is inversely correlated with neuronal activity (Xu et al., 2005). In addition, our findings are consistent with a very recent publication (available on-line) in male adolescents reporting that frontal cortex myo-inositol levels correlated negatively with subjective sleepiness and positively with total sleep time (Urrila et al., in press). We did not measure Homer1a expression in humans before and after sleep deprivation, nor, to our knowledge, has this been quantified in any other published study. Future work is warranted, to simultaneously quantify mGluR5 and Homer1a expression as a function of time awake.

When correlating myo-inositol concentrations with EEG power in the low frequency range, we found marginally significant associations with < 1-Hz activity (baseline: r = 0.47, p < 0.06; sleep deprivation: r = 0.60, p < 0.02). By contrast, the sleep deprivation-induced change in myo-inositol did not correlate significantly with the relative changes in single 0.25-Hz bins and the entire band within the 0.5-4.5 Hz range after sleep deprivation.

12) Three subjects were excluded from PET imaging, so why n =26 in subsection “mGluR5 availability predicts behavioral and EEG markers of sleep need in humans”?

We thank the reviewers for pointing out this mistake. It has been corrected in the revised manuscript (subsection “mGluR5 availability predicts behavioral and EEG markers of sleep need in humans”).

13) In a supplementary figure, can example metabolites be presented to better illustrate the criteria for exclusion/inclusion as described in subsection “Sleep deprivation-induced changes in brain metabolites reflect downstream markers of mGluR5 activation”?

To better illustrate the criteria for the selection of relevant metabolites, the statistical test results are summarized in the Table below. Only those metabolites that showed a significant alteration by sleep deprivation and, simultaneously, significant correlation with global mGluR5 availability in baseline and sleep deprivation conditions or with the sleep deprivation-induced increase in global mGluR5 availability, were considered relevant.

Competing interests

Funding

Swiss National Science Foundation (320030_135414)

Universität Zürich (Sleep and Health)

NCCR Neural Plasticity and Repair

Erich Seifritz

Hans-Peter Landolt

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported by the Swiss National Science Foundation grants # 135414 and # 163439 (to HPL) and # 146615 (to MT), the Clinical Research Priority Program of the University of Zürich 'Sleep and Health', and the National Center for Competence in Research 'Neural Plasticity and Repair'. We thank Dr. R Wehrle, Dr. R Dürr, Dr. V Bachmann, Th Berthold, Dr. C Klein, C Siegenthaler, S Röthlisberger, M Röthlisberger, C Schneider, C Laengle, S Jimenez, Y Emmenegger, and R Abbas for their help with data collection and analyses.

Ethics

Human subjects: All experimental procedures were conducted in accordance with the declaration of Helsinki (1964) and approved by the cantonal (ethics committee for research on human subjects of the canon of Zurich [Reference Nr. EK-Nr. 786] and Swiss federal authorities for research on human (Swiss Federal Institute of Public Health, Reference Nr. 464-0002-6/08.005701) subjects.

Animal experimentation: All animal experiments were carried out in accordance with the regulations of the Swiss Federal and State of Vaud Veterinary Offices (No. 2699.0).

Reviewing Editor

Louis J Ptáček, Reviewing Editor, University of California, San Francisco, United States

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